Advancing Alzheimer's disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study.

Journal: BMJ open
Published Date: Feb 8, 2025

Abstract

OBJECTIVES: Alzheimer's disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techniques.

Authors

  • Bingsheng Wang
    School of Nursing, Hangzhou Normal University, Hangzhou, China.
  • Ruihan Xie
    Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, China.
  • Wenhao Qi
    School of Nursing, Hangzhou Normal University, Hangzhou, China.
  • Jiani Yao
    School of Nursing, Hangzhou Normal University, Hangzhou, China.
  • Yankai Shi
    School of Nursing, Hangzhou Normal University, Hangzhou, China.
  • Xiajing Lou
    School of Nursing, Hangzhou Normal University, Hangzhou, China.
  • Chaoqun Dong
    Institute of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland.
  • Xiaohong Zhu
    School of Nursing, Hangzhou Normal University, Hangzhou, China.
  • Bing Wang
    Computer Science & Engineering Department at the University of Connecticut.
  • Danni He
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin,China.
  • Yanfei Chen
    School of Nursing, Hangzhou Normal University, Hangzhou, China.
  • Shihua Cao
    School of Nursing, Hangzhou Normal University, Hangzhou, China.

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